Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

Abstract

Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study. We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme. Fourteen percent of the topics were in the gender harassment theme, in which insulting, sexist, or degrading comments or behavior was directed towards women. Five percent of the topics involved sexual coercion (a benefit is offered in exchange for sexual favors), 5% involved sex discrimination, and 7% of the topics discussed retaliation against the victim for reporting the harassment, or for simply not complying with the harasser. Findings highlight the power differential between faculty and students, and the toll on students when professors abuse their power. While some topics did differ based on type of institution, there were no differences between the topics based on gender of harasser or field of study. This research can be beneficial to researchers in further investigation of this paper's dataset, and to policymakers in improving existing policies to create a safe and supportive environment in academia.

Seasonal characterization of diet discussions on Reddit

“…This approach categorizes topics by sorting words into clusters with high semantic similarity. Among the topic models, Latent Dirichlet Allocation model (LDA) (Blei, Ng, & Jordan, 2003) is the most established topic model (Lu, Mei, & Zhai, 2011) LDA is a popular technique that has been utilized for different research applications such as opinion mining (Karami & Elkouri, 2019;Hemsley, Erickson, Jarrahi, & Karami, 2020;Karami & Pendergraft, 2018;Karami, Shah, Vaezi, & Bansal, 2020), F I G U R E 1 Word frequency vs word rank-the vertical line shows the position of top-50 words reviewing literature (Shin et al, 2019;Karami, Lundy, Webb, & Dwivedi, 2020), analyzing sexual harassment stories (Karami, Swan, White, & Ford, 2019;Karami, White, Ford, Swan, & Spinel, 2020), exploring medical documents (Karami, Ghasemi, Sen, Moraes, & Shah, 2019) and health-related comments on social media (Karami & Shaw, 2019;Karami, Webb, & Kitzie, 2018). Applying LDA on a corpus provides two matrices.…”

“…Following the detection of topics, we then moved to qualitative analysis to inductively interpret and analyze conceptual themes (Karami, White et al, 2020). Our labeling was informed by reviewing the top related posts within each topic using P (T |D).…”

“Having More Women Humanitarian Leaders Will Help Transform The Humanitarian System”: Challenges and Opportunities for Women Leaders in Conflict and Humanitarian Health

“…(45) As for sexual harassment, most of the studies on harassment within academia are limited in sample size. Sexual harassment is underreported since many academic institutes lack a reliable and transparent reporting mechanism (76)(77)(78). Other factors contributing to underreporting include stigma, especially in conservative societies, fears of job loss in a highly competitive market, and power imbalances (79).…”

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

“…An example is represented by the finding that mindsets vehiculating positive emotions reach larger audiences on social media whereas negative emotional content can spread at faster rates (Ferrara and Yang, 2015). Another example for the relevance of mindset reconstruction is uncovering and acting upon traces of science anxiety in student populations in order to improve their learning experiences (Stella, 2020;Stella and Zaytseva, 2020) or detecting sexual harassment through large-scale web surveys (Karami et al, 2020).…”

“…The authors investigated online self-reports of sexual harassment experiences and through a topic analysis they highlighted evidence for sexual harassment in academia mainly targeting women and involving coercion, gender discrimination and retaliation. Building upon the knowledge extraction approach of Karami et al (Karami et al, 2020), this study shifts its attention from explicit sexual harassment to the larger topic of gender biases in science, which includes harassment itself but also implicit biases (Shapiro and Williams, 2012), gender pay gaps (Courey and Heywood, 2018) and stereotypical perceptions about leadership (Pennington et al, 2016;Ely et al, 2011). Furthermore, rather than focusing on self-reports, this study aims at tackling a different information system, namely Twitter, where social users can engage in social discourse and reach large audiences (Welles and González-Bailón, 2020).…”